Data Driven Amateur Astronomy

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Houston Astronomical Society December 5th, 2025

Modern astronomy research has become data-driven. Using data science techniques alongside computation allows us to interrogate data to understand astrophysical phenomena. The explosion of data sets has opened up new ways for enterprising amateur astronomers to contribute to modern astronomical research. Data can come from large-scale surveys, space-based observatories, individual scientists, or students. You can learn to select, reduce, visualize, and interpret authentic astronomical data while applying data science techniques to construct astronomy knowledge. Many free web-based tools leverage data science techniques. This talk explores how these activities bridge the gap between data science and astronomy, enabling amateurs to learn about both simultaneously.

If you are interested in data-driven astronomy learning, check out the page below from a talk given at the first-ever Data Science Education in K12 Conference. Even though the materials shared were designed for teaching high school astronomy, enterprising amateur astronomers can still pick up some cool tricks.

Integrating Data Science Pedagogy into Astronomy Courses

HOU’s Coding: Using CS for Science Teaching

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Integrating Computer Science into Science Teaching

When doing domain-specific programming in science, some CS pedagogy can be used to
scaffold concepts like conditionals, function writing, and looping. Using worked examples,
minimally working programs, sub-task labeling, and live coding can help a student bring
coding to bear on learning concepts in science. Room B107 1:15 – 2:15 pm CDT

WeTeach_CS Summit 2025

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Impacting Student Attitudes About CS Through Coding Integration into Science

In this session, participants will learn how integrating computer science (CS) pedagogy into science and math classes can positively impact students’ attitudes about computer science. When students experience lessons in science and math classes that incorporate computer science (CS), they feel more confident about CS. They are more likely to pursue academic pathways that include CS. Participants will see examples of specific lessons that can be used in science and math courses to improve students’ attitudes about the use of computer science (CS) in non-CS STEM courses.

Participants will use STEMcoding during the session and can access STEMcoding for free for one year.

If you are interested in more academic details about the STEMcoding study, click here.

TSAAPT/TSAPS Spring 2025: Modeling with STEMcoding in AP Physics 1

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Improving Student Computational Thinking Outcome Expectancy through Integrating Model-building in a Secondary Physics Course

The role of computation in physics is increasing in both a professional and educational context. High school physics pedagogy should incorporate computational thinking, data science, and computer science skills to bolster learning across these domains. The study described here shows that when students build physically meaningful models using computer programming in a physics course, their outcome expectancy is positively impacted. Outcome expectancy can predict students’ future academic and professional choices. The STEMcoding platform asks students to construct models using Euler-Cromer step-wise modeling of physical phenomena, such as those described by the laws of motion. The study presented had students use STEMcoding during the kinematics portion of Advanced Placement Physics 1 classes at a large, urban high school in southeast Texas. A moderate positive impact on outcome expectancy was found across various groups in the study population. Results show how computational essays can provide scaffolded learning while allowing students to use code to create interactive models, allowing knowledge construction in a domain-specific manner.

Bridging Research and the Classroom: Leveraging Multi-Wavelength Data for SED Plots of Young Stellar Objects Using Google Colab (AAS 245 WM)

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Modern astronomy research requires computation and data science tools, which are not traditionally part of the Astronomy 101 course. This computational essay describes what a spectral energy distribution (SED) is and uses code to construct one. Although existing computational thinking tools like spreadsheets can be leveraged to allow computational thinking to construct science knowledge, the needs of a student or teacher astronomical researcher will likely go beyond introductory tools. The goal of the SED computational essay presented is not only to inform about SEDs and their use but also to be a part of a toolset meant to allow students and teachers to interrogate other stellar photometric datasets at scale. This notebook was designed by alumni of the NASA/IPAC Teacher Archive Research Project (NITARP). The code can be found on GitHub. This presentation is in the iPoster format, as seen at this link.

More details about this project are available.